Redundancy Techniques for Straggler Mitigation in Distributed Optimization and Learning

By Can Bülent Karakus, Yifan Sun, Suhas N. Diggavi, Wotao Yin. 2018

55 citations recorded by the source corpus.

Performance of distributed optimization and learning systems is bottlenecked by "straggler" nodes and slow communication links, which significantly delay computation. We propose a distributed optimization framework where the dataset is "encoded" to have an over-complete representation with built-in redundancy, and the straggling nodes in the system are dynamically left out of the computation at every iteration, whose loss is compensated by the embedded redundancy. We show that oblivious application of several popular optimization algorithms on encoded data, including gradient descent, L-BFGS, proximal gradient under data parallelism, and coordinate descent under model parallelism, converge to either approximate or exact solutions of the original problem when stragglers are treated as erasures. These convergence results are deterministic, i.e., they establish sample path convergence for arbitrary sequences of delay patterns or distributions on the nodes, and are independent of the tail behavior of the delay distribution. We demonstrate that equiangular tight frames have desirable properties as encoding matrices, and propose efficient mechanisms for encoding large-scale data. We implement the proposed technique on Amazon EC2 clusters, and demonstrate its performance over several learning problems, including matrix factorization, LASSO, ridge regression and logistic regression, and compare the proposed method with uncoded, asynchronous, and data replication strategies.

Subfields: Artificial Intelligence, Computational Mechanics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Deepesh Data
2. Suhas N. Diggavi
3. Linqi Song
4. Amir Salman Avestimehr
5. Osvaldo Simeone
6. Saurav Prakash
7. Jinhyun So
8. Basak Guler

In Computational Mechanics

1. Deepesh Data
2. Linqi Song
3. Suhas N. Diggavi
4. Amir Salman Avestimehr
5. Osvaldo Simeone
6. Saurav Prakash

Citations in the corpus

• Data Encoding for Byzantine-Resilient Distributed Optimization (2019, background): “[48] that, in addition to distributed GD, also studies distributed CD, and that for quadratic problems (e.”
• A Very Brief Introduction to Machine Learning With Applications to Communication Systems (2018, background): “A recent line of work investigates the resulting interplay between computation and communication [80].”
• Coded Computing for Federated Learning at the Edge (2020, background): “(Karakus et al., 2019) proposed to encode over the data for avoiding the impact of stragglers for linear regression tasks.”